/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2019, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
#pragma once

#include <mrpt/poses/CPointPDF.h>
#include <mrpt/poses/CPointPDFGaussian.h>
#include <mrpt/math/CMatrix.h>
#include <mrpt/math/CMatrixD.h>

namespace mrpt::poses
{
/** Declares a class that represents a Probability Density function (PDF) of a
 * 3D point \f$ p(\mathbf{x}) = [x ~ y ~ z ]^t \f$.
 *   This class implements that PDF as the following multi-modal Gaussian
 * distribution:
 *
 * \f$ p(\mathbf{x}) = \sum\limits_{i=1}^N \omega^i \mathcal{N}( \mathbf{x} ;
 * \bar{\mathbf{x}}^i, \mathbf{\Sigma}^i )  \f$
 *
 *  Where the number of modes N is the size of CPointPDFSOG::m_modes
 *
 *  See mrpt::poses::CPointPDF for more details.
 *
 * \sa CPointPDF, CPosePDF,
 * \ingroup poses_pdf_grp
 */
class CPointPDFSOG : public CPointPDF
{
	DEFINE_SERIALIZABLE(CPointPDFSOG)

   public:
	/** The struct for each mode:
	 */
	struct TGaussianMode
	{
		TGaussianMode() : val() {}
		CPointPDFGaussian val;

		/** The log-weight
		 */
		double log_w{0};
	};

	using CListGaussianModes = std::deque<TGaussianMode>;
	using const_iterator = std::deque<TGaussianMode>::const_iterator;
	using iterator = std::deque<TGaussianMode>::iterator;

   protected:
	/** Assures the symmetry of the covariance matrix (eventually certain
	 * operations in the math-coprocessor lead to non-symmetric matrixes!)
	 */
	void assureSymmetry();

	/** The list of SOG modes */
	CListGaussianModes m_modes;

   public:
	/** Default constructor
	 * \param nModes The initial size of CPointPDFSOG::m_modes
	 */
	CPointPDFSOG(size_t nModes = 1);

	/** Clear all the gaussian modes */
	void clear();

	/** Access to individual beacons */
	const TGaussianMode& operator[](size_t i) const
	{
		ASSERT_(i < m_modes.size());
		return m_modes[i];
	}
	/** Access to individual beacons */
	TGaussianMode& operator[](size_t i)
	{
		ASSERT_(i < m_modes.size());
		return m_modes[i];
	}

	/** Access to individual beacons */
	const TGaussianMode& get(size_t i) const
	{
		ASSERT_(i < m_modes.size());
		return m_modes[i];
	}
	/** Access to individual beacons */
	TGaussianMode& get(size_t i)
	{
		ASSERT_(i < m_modes.size());
		return m_modes[i];
	}

	/** Inserts a copy of the given mode into the SOG */
	void push_back(const TGaussianMode& m) { m_modes.push_back(m); }
	iterator begin() { return m_modes.begin(); }
	iterator end() { return m_modes.end(); }
	const_iterator begin() const { return m_modes.begin(); }
	const_iterator end() const { return m_modes.end(); }
	iterator erase(iterator i) { return m_modes.erase(i); }
	/** Resize the number of SOG modes */
	void resize(const size_t N);
	/** Return the number of Gaussian modes. */
	size_t size() const { return m_modes.size(); }
	/** Return whether there is any Gaussian mode. */
	bool empty() const { return m_modes.empty(); }
	/** Returns an estimate of the point, (the mean, or mathematical expectation
	 * of the PDF) \sa getCovariance   */
	void getMean(CPoint3D& mean_point) const override;

	/** Returns an estimate of the point covariance matrix (3x3 cov matrix) and
	 * the mean, both at once. \sa getMean */
	void getCovarianceAndMean(
		mrpt::math::CMatrixDouble33& cov, CPoint3D& mean_point) const override;

	/** Normalize the weights in m_modes such as the maximum log-weight is 0 */
	void normalizeWeights();

	/** Return the Gaussian mode with the highest likelihood (or an empty
	 * Gaussian if there are no modes in this SOG) */
	void getMostLikelyMode(CPointPDFGaussian& outVal) const;

	/** Computes the "Effective sample size" (typical measure for Particle
	 * Filters), applied to the weights of the individual Gaussian modes, as a
	 * measure of the equality of the modes (in the range [0,total # of modes]).
	 */
	double ESS() const;

	/** Copy operator, translating if necesary (for example, between particles
	 * and gaussian representations) */
	void copyFrom(const CPointPDF& o) override;

	/** Save the density to a text file, with the following format:
	 *  There is one row per Gaussian "mode", and each row contains 10
	 * elements:
	 *   - w (The weight)
	 *   - x_mean (gaussian mean value)
	 *   - y_mean (gaussian mean value)
	 *   - x_mean (gaussian mean value)
	 *   - C11 (Covariance elements)
	 *   - C22 (Covariance elements)
	 *   - C33 (Covariance elements)
	 *   - C12 (Covariance elements)
	 *   - C13 (Covariance elements)
	 *   - C23 (Covariance elements)
	 *
	 */
	bool saveToTextFile(const std::string& file) const override;

	/** this = p (+) this. This can be used to convert a PDF from local
	 * coordinates to global, providing the point (newReferenceBase) from which
	 *   "to project" the current pdf. Result PDF substituted the currently
	 * stored one in the object. */
	void changeCoordinatesReference(const CPose3D& newReferenceBase) override;

	/** Draw a sample from the pdf. */
	void drawSingleSample(CPoint3D& outSample) const override;

	/** Bayesian fusion of two point distributions (product of two
	 * distributions->new distribution), then save the result in this object
	 * (WARNING: See implementing classes to see classes that can and cannot be
	 * mixtured!)
	 * \param p1 The first distribution to fuse
	 * \param p2 The second distribution to fuse
	 * \param minMahalanobisDistToDrop If set to different of 0, the result of
	 * very separate Gaussian modes (that will result in negligible components)
	 * in SOGs will be dropped to reduce the number of modes in the output.
	 */
	void bayesianFusion(
		const CPointPDF& p1, const CPointPDF& p2,
		const double minMahalanobisDistToDrop = 0) override;

	/** Evaluates the PDF within a rectangular grid and saves the result in a
	 * matrix (each row contains values for a fixed y-coordinate value).
	 */
	void evaluatePDFInArea(
		float x_min, float x_max, float y_min, float y_max, float resolutionXY,
		float z, mrpt::math::CMatrixD& outMatrix, bool sumOverAllZs = false);

	/** Evaluates the PDF at a given point */
	double evaluatePDF(const CPoint3D& x, bool sumOverAllZs) const;

};  // End of class def.
}  // namespace mrpt::poses
